1. Field of the Invention
The present invention relates to a method of detecting forest fire smoke. More particularly, the present invention relates to a forest fire smoke detection method using random forest classification.
2. Description of the Related Art
Recently, the occurrence of forest fires not only destroys the surrounding environment or the local ecosystem but also potentially puts the living space of human beings in danger. Accordingly, technology for detecting the onset of a forest fire early on has been developed in preparation for the occurrence of a forest fire. In particular, as large-scale forest fires frequently break out all around the world, the necessity for an automatic alarm system has increased that can detect a forest fire in its early stage and that can provide an alarm for the occurrence of the forest fire.
Typically, forest fire detection systems have been configured based on a method in which a watchtower has been built to survey an area where there is the possibility of a forest fire breaking out and a fire watcher working on the watchtower visually determines with the naked eye whether a forest fire has broken out. However, since such a visual detection method requires enormous manpower resources for watching, there have been proposed a variety of technologies for replacing the visual detection method. These technologies may include, for example, a method of installing an infrared sensor that detects the thermal energy of flames, a method of installing Light Detection And Ranging (LIDAR) equipment that measures the intensity of laser light back-scattered by smoke particles, a method of installing a thermal imaging sensor that measures the temperature of flames, etc. However, such optical equipment is problematic in that it is expensive and frequently causes errors due to atmospheric phenomena, such as clouds, fog, or yellow sand, and light scattering, and in that it is difficult for the optical equipment to operate normally when the distance from the sensor to an ignition point increases.
Therefore, recently, as shown in FIG. 1, a method has attracted attention in which video camera equipment, which includes a Charge Coupled Device (CCD) and which is inexpensive and enables remote detection, is installed at the top of the watchtower to detect forest fires. Forest fire detection using CCD video camera equipment is classified into a method of detecting flames and a method of detecting smoke. In the early stage of a forest fire breaking out, the size of the flames is much smaller than the distance from the camera to the ignition point, and thus it is very difficult to detect such flames. In contrast, smoke is characterized in that it is generated earlier than flames and is diffused to a much wider area than are flames. Accordingly, a forest fire detection technology based on smoke detection has been mainly developed.
However, unlike flames, smoke diffuses slowly and the shape and color thereof are vague in general, thus making it difficult to definitely identify smoke. Smoke detection technologies that have been proposed to date can improve the precision of smoke detection when Fuzzy Finite Automata (FFA) and the temporal variation characteristics of images are used. However, since the dimension of a transition matrix used in the related computation increases in proportion to the improvement in precision, there is a concomitant increase in the required memory and computation time. Further, even if a Hidden Markov Model (HMM) is used, there are limitations in that boundary values must be individually and heuristically set so as to determine the states in which images are detected at specific times.
Therefore, a need exists for a system and method for performing a self diagnosis of a device without the inconvenience caused when manually selecting a self diagnosis item from a computer or a user interface.